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Found 8 papers, 0 papers with code
DRIVE: Machine Learning to Identify Drivers of Cancer with High-Dimensional Genomic Data & Imputed Labels
Identifying the mutations that drive cancer growth is key in clinical decision making and precision oncology.
Deep Bayesian Recurrent Neural Networks for Somatic Variant Calling in Cancer
Although these methods provide greater accuracy, classic neural networks lack the ability to indicate the confidence of a variant call.
Safety and Robustness in Decision Making: Deep Bayesian Recurrent Neural Networks for Somatic Variant Calling in Cancer
The genomic profile underlying an individual tumor can be highly informative in the creation of a personalized cancer treatment strategy for a given patient; a practice known as precision oncology.
Effective Sub-clonal Cancer Representation to Predict Tumor Evolution
Our proposed method is based on the intuition that if we can capture the true characteristics of sub-clones within a tumor and represent it in the form of features, a sophisticated machine learning algorithm can be trained to predict its behavior.
Flatsomatic: A Method for Compression of Somatic Mutation Profiles in Cancer
In this study, we present Flatsomatic - a Variational Auto Encoder (VAE) optimized to compress somatic mutations that allow for unbiased data compression whilst maintaining the signal.
Learning Embeddings from Cancer Mutation Sets for Classification Tasks
Thus, the creation of low dimensional representations of somatic mutation profiles that hold useful information about the DNA of cancer cells will facilitate the use of such data in applications that will progress precision medicine.
Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection
DNA sequencing to identify genetic variants is becoming increasingly valuable in clinical settings.
A Framework for Implementing Machine Learning on Omics Data
The potential benefits of applying machine learning methods to -omics data are becoming increasingly apparent, especially in clinical settings.
